{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib as mpl \n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.datasets import make_blobs\n",
    "from sklearn import datasets\n",
    "\n",
    "from yellowbrick.cluster import KElbowVisualizer, SilhouetteVisualizer\n",
    "\n",
    "mpl.rcParams[\"figure.figsize\"] = (9,6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Yellowbrick &mdash; Clustering Evaluation Examples\n",
    "\n",
    "The Yellowbrick library is a diagnostic visualization platform for machine learning that allows data scientists to steer the model selection process. It extends the scikit-learn API with a new core object: the `Visualizer`. Visualizers allow models to be fit and transformed as part of the scikit-learn pipeline process, providing visual diagnostics throughout the transformation of high-dimensional data.\n",
    "\n",
    "In machine learning, clustering models are unsupervised methods that attempt to detect patterns in unlabeled data. There are two primary classes of clustering algorithms: *agglomerative* clustering which links similar data points together, and *centroidal* clustering which attempts to find centers or partitions in the data.\n",
    "\n",
    "Currently, Yellowbrick provides two visualizers to evaluate *centroidal* mechanisms, particularly K-Means clustering, that help users discover an optimal $K$ parameter in the clustering metric:\n",
    "- `KElbowVisualizer`  visualizes the clusters according to a scoring function, looking for an \"elbow\" in the curve. \n",
    "- `SilhouetteVisualizer`  visualizes the silhouette scores of each cluster in a single model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load the Data\n",
    "\n",
    "For the following examples, we'll use the widely famous Iris dataset. The data set contains 3 classes of 50 instances each, where each class refers to a type of iris plant. You can learn more about it here: [Iris Data Set](https://archive.ics.uci.edu/ml/datasets/iris)\n",
    "\n",
    "The dataset is loaded using scikit-learn's `datasets.load_iris()` function to create a sample two-dimensional dataset with 8 random clusters of points."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load iris flower dataset\n",
    "iris = datasets.load_iris()\n",
    "\n",
    "X = iris.data #clustering is unsupervised learning hence we load only X(i.e.iris.data) and not Y(i.e. iris.target)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Let's have a look at the dataset\n",
    "\n",
    "Before we dive into how this data can be evaluated efficiently using Yellowbrick, let's have a look at how the clusters actually look."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4.4</td>\n",
       "      <td>2.9</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)\n",
       "0                5.1               3.5                1.4               0.2\n",
       "1                4.9               3.0                1.4               0.2\n",
       "2                4.7               3.2                1.3               0.2\n",
       "3                4.6               3.1                1.5               0.2\n",
       "4                5.0               3.6                1.4               0.2\n",
       "5                5.4               3.9                1.7               0.4\n",
       "6                4.6               3.4                1.4               0.3\n",
       "7                5.0               3.4                1.5               0.2\n",
       "8                4.4               2.9                1.4               0.2\n",
       "9                4.9               3.1                1.5               0.1"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Converting the data into dataframe\n",
    "feature_names = iris.feature_names\n",
    "iris_dataframe = pd.DataFrame(X, columns=feature_names)\n",
    "iris_dataframe.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### K-Means Algorithm\n",
    "K-Means is a simple unsupervised machine learning algorithm that groups data into the number $K$ of clusters specified by the user, even if it is not the optimal number of clusters for the dataset. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<mpl_toolkits.mplot3d.art3d.Path3DCollection at 0x122ca2828>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Fitting the model with a dummy model, with 3 clusters (we already know there are 3 classes in the Iris dataset)\n",
    "k_means = KMeans(n_clusters=3)\n",
    "k_means.fit(X)\n",
    "\n",
    "# Plotting a 3d plot using matplotlib to visualize the data points\n",
    "fig = plt.figure(figsize=(7,7))\n",
    "ax = fig.add_subplot(111, projection='3d')\n",
    "\n",
    "# Setting the colors to match cluster results\n",
    "colors = ['red' if label == 0 else 'purple' if label==1 else 'green' for label in k_means.labels_]\n",
    "\n",
    "ax.scatter(X[:,3], X[:,0], X[:,2], c=colors)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the above example plot, one of the clusters is linearly seperable and at a good seperation from other two clusters. Two of the clusters are close by and not linearly seperable.\n",
    "\n",
    "Also the dataset is 4-dimensional i.e. it has 4 features, but for the sake of visualization using `matplotlib`, one of dimensions has been ignored. Therefore, it can be said that just visualization of data-points is not always enough for knowing optimal number of clusters $K$. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Elbow Method \n",
    "\n",
    "Yellowbrick's `KElbowVisualizer` implements the “elbow” method of selecting the optimal number of clusters by fitting the K-Means model with a range of values for $K$. If the line chart looks like an arm, then the “elbow” (the point of inflection on the curve) is a good indication that the underlying model fits best at that point.\n",
    "\n",
    "In the following example, the `KElbowVisualizer` fits the model for a range of $K$ values from 2 to 10, which is set by the parameter `k=(2,11)`. When the model is fit with 3 clusters we can see an \"elbow\" in the graph, which in this case we know to be the optimal number since our dataset has 3 clusters of points. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Instantiate the clustering model and visualizer\n",
    "model = KMeans()\n",
    "visualizer = KElbowVisualizer(model, k=(2,11))\n",
    "\n",
    "visualizer.fit(X)    # Fit the data to the visualizer\n",
    "visualizer.poof()    # Draw/show/poof the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By default, the scoring parameter `metric` is set to `distortion`, which computes the sum of squared distances from each point to its assigned center. However, two other metrics can also be used with the `KElbowVisualizer`&mdash;`silhouette` and `calinski_harabaz`. The `silhouette` score is the mean silhouette coefficient for all samples, while the `calinski_harabaz` score computes the ratio of dispersion between and within clusters.\n",
    " \n",
    "The `KElbowVisualizer` also displays the amount of time to fit the model per $K$, which can be hidden by setting `timings=False`. In the following example, we'll use the `calinski_harabaz` score and hide the time to fit the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Instantiate the clustering model and visualizer \n",
    "model = KMeans()\n",
    "visualizer = KElbowVisualizer(model, k=(2,11), metric='calinski_harabaz', timings=False)\n",
    "\n",
    "visualizer.fit(X)    # Fit the data to the visualizer\n",
    "visualizer.poof()    # Draw/show/poof the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is important to remember that the Elbow method does not work well if the data is not very clustered. In such cases, you might see a smooth curve and the optimal value of $K$ will be unclear.\n",
    "\n",
    "You can learn more about the Elbow method at Robert Grove's [Blocks](https://bl.ocks.org/rpgove/0060ff3b656618e9136b)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Silhouette Visualizer \n",
    "\n",
    "Silhouette analysis can be used to evaluate the density and separation between clusters. The score is calculated by averaging the silhouette coefficient for each sample, which is computed as the difference between the average intra-cluster distance and the mean nearest-cluster distance for each sample, normalized by the maximum value. This produces a score between -1 and +1, where scores near +1 indicate high separation and scores near -1 indicate that the samples may have been assigned to the wrong cluster.\n",
    "\n",
    "The `SilhouetteVisualizer` displays the silhouette coefficient for each sample on a per-cluster basis, allowing users to visualize the density and separation of the clusters. This is particularly useful for determining cluster imbalance or for selecting a value for $K$ by comparing multiple visualizers.\n",
    "\n",
    "Since we created the sample dataset for these examples, we already know that the data points are grouped into 8 clusters. So for the first `SilhouetteVisualizer` example, we'll set $K$ to 3 in order to show how the plot looks when using the optimal value of $K$. \n",
    "\n",
    "Notice that graph contains homogeneous and long silhouettes. In addition, the vertical red-dotted line on the plot indicates the average silhouette score for all observations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Instantiate the clustering model and visualizer \n",
    "model = KMeans(3)\n",
    "visualizer = SilhouetteVisualizer(model)\n",
    "\n",
    "visualizer.fit(X)    # Fit the data to the visualizer\n",
    "visualizer.poof()    # Draw/show/poof the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the next example, let's see what happens when using a non-optimal value for $K$, in this case, 6. \n",
    "\n",
    "Now we see that the width of clusters 1 to 6 have become narrow, of unequal width and their silhouette coefficient scores have dropped. This occurs because the width of each silhouette is proportional to the number of samples assigned to the cluster. The model is trying to fit our data into a larger than optimal number of clusters, making some of the clusters narrower but much less cohesive as seen from the drop in average-silhouette score."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Instantiate the clustering model and visualizer \n",
    "model = KMeans(6)\n",
    "visualizer = SilhouetteVisualizer(model)\n",
    "\n",
    "visualizer.fit(X)    # Fit the data to the visualizer\n",
    "visualizer.poof()    # Draw/show/poof the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## After Gopal's improvements to Silhouette Visualizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from yellowbrick.style import color_palette\n",
    "from yellowbrick.cluster.base import ClusteringScoreVisualizer\n",
    "\n",
    "from sklearn.metrics import silhouette_score, silhouette_samples\n",
    "\n",
    "\n",
    "## Packages for export\n",
    "__all__ = [\n",
    "    \"SilhouetteVisualizer\"\n",
    "]\n",
    "\n",
    "\n",
    "##########################################################################\n",
    "## Silhouette Method for K Selection\n",
    "##########################################################################\n",
    "\n",
    "class SilhouetteVisualizer(ClusteringScoreVisualizer):\n",
    "    \"\"\"\n",
    "    The Silhouette Visualizer displays the silhouette coefficient for each\n",
    "    sample on a per-cluster basis, visually evaluating the density and\n",
    "    separation between clusters. The score is calculated by averaging the\n",
    "    silhouette coefficient for each sample, computed as the difference\n",
    "    between the average intra-cluster distance and the mean nearest-cluster\n",
    "    distance for each sample, normalized by the maximum value. This produces a\n",
    "    score between -1 and +1, where scores near +1 indicate high separation\n",
    "    and scores near -1 indicate that the samples may have been assigned to\n",
    "    the wrong cluster.\n",
    "\n",
    "    In SilhouetteVisualizer plots, clusters with higher scores have wider\n",
    "    silhouettes, but clusters that are less cohesive will fall short of the\n",
    "    average score across all clusters, which is plotted as a vertical dotted\n",
    "    red line.\n",
    "\n",
    "    This is particularly useful for determining cluster imbalance, or for\n",
    "    selecting a value for K by comparing multiple visualizers.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    model : a Scikit-Learn clusterer\n",
    "        Should be an instance of a centroidal clustering algorithm (``KMeans``\n",
    "        or ``MiniBatchKMeans``).\n",
    "\n",
    "    ax : matplotlib Axes, default: None\n",
    "        The axes to plot the figure on. If None is passed in the current axes\n",
    "        will be used (or generated if required).\n",
    "\n",
    "    kwargs : dict\n",
    "        Keyword arguments that are passed to the base class and may influence\n",
    "        the visualization as defined in other Visualizers.\n",
    "\n",
    "    Attributes\n",
    "    ----------\n",
    "    silhouette_score_ : float\n",
    "        Mean Silhouette Coefficient for all samples. Computed via scikit-learn\n",
    "        `sklearn.metrics.silhouette_score`.\n",
    "\n",
    "    silhouette_samples_ : array, shape = [n_samples]\n",
    "        Silhouette Coefficient for each samples. Computed via scikit-learn\n",
    "        `sklearn.metrics.silhouette_samples`.\n",
    "\n",
    "    n_samples_ : integer\n",
    "        Number of total samples in the dataset (X.shape[0])\n",
    "\n",
    "    n_clusters_ : integer\n",
    "        Number of clusters (e.g. n_clusters or k value) passed to internal\n",
    "        scikit-learn model.\n",
    "\n",
    "    Examples\n",
    "    --------\n",
    "\n",
    "    >>> from yellowbrick.cluster import SilhouetteVisualizer\n",
    "    >>> from sklearn.cluster import KMeans\n",
    "    >>> model = SilhouetteVisualizer(KMeans(10))\n",
    "    >>> model.fit(X)\n",
    "    >>> model.poof()\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, model, ax=None, **kwargs):\n",
    "        super(SilhouetteVisualizer, self).__init__(model, ax=ax, **kwargs)\n",
    "\n",
    "        # Visual Properties\n",
    "        # TODO: Fix the color handling\n",
    "        self.colormap = kwargs.get('colormap', 'set1')\n",
    "        self.color = kwargs.get('color', None)\n",
    "\n",
    "    def fit(self, X, y=None, **kwargs):\n",
    "        \"\"\"\n",
    "        Fits the model and generates the silhouette visualization.\n",
    "        \"\"\"\n",
    "        # TODO: decide to use this method or the score method to draw.\n",
    "        # NOTE: Probably this would be better in score, but the standard score\n",
    "        # is a little different and I'm not sure how it's used.\n",
    "\n",
    "        # Fit the wrapped estimator\n",
    "        self.estimator.fit(X, y, **kwargs)\n",
    "\n",
    "        # Get the properties of the dataset\n",
    "        self.n_samples_ = X.shape[0]\n",
    "        self.n_clusters_ = self.estimator.n_clusters\n",
    "\n",
    "        # Compute the scores of the cluster\n",
    "        labels = self.estimator.predict(X)\n",
    "        self.silhouette_score_ = silhouette_score(X, labels)\n",
    "        self.silhouette_samples_ = silhouette_samples(X, labels)\n",
    "\n",
    "        # Draw the silhouette figure\n",
    "        self.draw(labels)\n",
    "\n",
    "        # Return the estimator\n",
    "        return self\n",
    "\n",
    "    def draw(self, labels):\n",
    "        \"\"\"\n",
    "        Draw the silhouettes for each sample and the average score.\n",
    "\n",
    "        Parameters\n",
    "        ----------\n",
    "\n",
    "        labels : array-like\n",
    "            An array with the cluster label for each silhouette sample,\n",
    "            usually computed with ``predict()``. Labels are not stored on the\n",
    "            visualizer so that the figure can be redrawn with new data.\n",
    "        \"\"\"\n",
    "\n",
    "        # Track the positions of the lines being drawn\n",
    "        y_lower = 10 # The bottom of the silhouette\n",
    "\n",
    "        # Get the colors from the various properties\n",
    "        # TODO: Use resolve_colors instead of this\n",
    "        colors = color_palette(self.colormap, self.n_clusters_)\n",
    "\n",
    "        # For each cluster, plot the silhouette scores\n",
    "        for idx in range(self.n_clusters_):\n",
    "\n",
    "            # Collect silhouette scores for samples in the current cluster .\n",
    "            values = self.silhouette_samples_[labels == idx]\n",
    "            values.sort()\n",
    "\n",
    "            # Compute the size of the cluster and find upper limit\n",
    "            size = values.shape[0]\n",
    "            y_upper = y_lower + size\n",
    "\n",
    "            color = colors[idx]\n",
    "            self.ax.fill_betweenx(\n",
    "                np.arange(y_lower, y_upper), 0, values,\n",
    "                facecolor=color, edgecolor=color, alpha=0.5\n",
    "            )\n",
    "\n",
    "            # Label the silhouette plots with their cluster numbers\n",
    "            self.ax.text(-0.05, y_lower + 0.5 * size, str(idx))\n",
    "\n",
    "            # Compute the new y_lower for next plot\n",
    "            y_lower = y_upper + 10\n",
    "\n",
    "        # The vertical line for average silhouette score of all the values\n",
    "        self.ax.axvline(\n",
    "            x=self.silhouette_score_, color=\"red\", linestyle=\"--\"\n",
    "        )\n",
    "\n",
    "        return self.ax\n",
    "\n",
    "    def finalize(self):\n",
    "        \"\"\"\n",
    "        Prepare the figure for rendering by setting the title and adjusting\n",
    "        the limits on the axes, adding labels and a legend.\n",
    "        \"\"\"\n",
    "\n",
    "        # Set the title\n",
    "        self.set_title((\n",
    "            \"Silhouette Plot of {} Clustering for {} Samples in {} Centers\"\n",
    "        ).format(\n",
    "            self.name, self.n_samples_, self.n_clusters_\n",
    "        ))\n",
    "\n",
    "        # Set the X and Y limits\n",
    "        # The silhouette coefficient can range from -1, 1;\n",
    "        # but here we scale the plot according to our visualizations\n",
    "\n",
    "        # l_xlim and u_xlim are lower and upper limits of the x-axis,\n",
    "        # set according to our calculated maximum and minimum silhouette score along with necessary padding\n",
    "        l_xlim = max(-1, min(-0.1, round(min(self.silhouette_samples_) - 0.1, 1)))\n",
    "        u_xlim = min(1, round(max(self.silhouette_samples_) + 0.1, 1))\n",
    "        self.ax.set_xlim([l_xlim, u_xlim])\n",
    "\n",
    "        # The (n_clusters_+1)*10 is for inserting blank space between\n",
    "        # silhouette plots of individual clusters, to demarcate them clearly.\n",
    "        self.ax.set_ylim([0, self.n_samples_ + (self.n_clusters_ + 1) * 10])\n",
    "\n",
    "        # Set the x and y labels\n",
    "        self.ax.set_xlabel(\"silhouette coefficient values\")\n",
    "        self.ax.set_ylabel(\"cluster label\")\n",
    "\n",
    "        # Set the ticks on the axis object.\n",
    "        self.ax.set_yticks([])  # Clear the yaxis labels / ticks\n",
    "        self.ax.xaxis.set_major_locator(plt.MultipleLocator(0.1))  # Set the ticks at multiples of 0.1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Instantiate the clustering model and visualizer \n",
    "model = KMeans(6)\n",
    "visualizer = SilhouetteVisualizer(model)\n",
    "\n",
    "visualizer.fit(X)    # Fit the data to the visualizer\n",
    "visualizer.poof()    # Draw/show/poof the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
